American Idol data
This week we’re exploring American Idol data! This is a comprehensive dataset put together by kkakey .
There’s so much data! What do you want to know about American Idol? Song choices, TV ratings, characteristics of winners?
Data in this dataset comes from Wikipedia . Data collected on seasons 1-18 of American Idol.
The Datasets * songs.csv - songs that contestants sang and competed with on American Idol from seasons 1-18 * auditions.csv - audition, cities, dates, and venues * elimination_chart.csv - eliminations by week. Data availability varies season-to-season based on season length and number of finalists competing * finalists.csv - information on top contestants, including birthday, hometown, and description * ratings.csv - episode ratings and views. * seasons.csv - season-level information, including season winner, runner-up, release dates, and judges
The Data
# Option 1: tidytuesdayR package ## install.packages("tidytuesdayR") <- tidytuesdayR:: tt_load ('2024-07-23' )## OR <- tidytuesdayR:: tt_load (2024 , week = 30 )<- tuesdata$ auditions<- tuesdata$ eliminations<- tuesdata$ finalists<- tuesdata$ ratings<- tuesdata$ seasons<- tuesdata$ songs# Option 2: Read directly from GitHub <- readr:: read_csv ('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-07-23/auditions.csv' )<- readr:: read_csv ('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-07-23/eliminations.csv' )<- readr:: read_csv ('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-07-23/finalists.csv' )<- readr:: read_csv ('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-07-23/ratings.csv' )<- readr:: read_csv ('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-07-23/seasons.csv' )<- readr:: read_csv ('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-07-23/songs.csv' )
How to Participate
Explore the data , watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.Create a visualization, a model, a shiny app , or some other piece of data-science-related output, using R or another programming language.
Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
auditions.csv
season
double
Season
audition_date_start
double
Start date of audition
audition_date_end
double
End date of audition
audition_city
character
City where audition took place
audition_venue
character
Preliminary location where auditions took place
episodes
character
Episode numbers at this audition location
episode_air_date
character
Date episode aired
callback_venue
character
Filming and callback location where auditions took place
callback_date_start
double
Start date of callback audition
callback_date_end
double
End date of callback audition
tickets_to_hollywood
double
Number of contestants selected from audition to go to Hollywood week
guest_judge
character
Name of guest judge at audition
eliminations.csv
season
double
Season Number
place
character
Place (or place range) contestant finished in competition
gender
character
Gender of contestant
contestant
character
Competitor name
top_36
character
Top 36 eliminations
top_36_2
character
Top 36 eliminations (week 2)
top_36_3
character
Top 36 eliminations (week 3)
top_36_4
character
Top 36 eliminations (week 4)
top_32
character
Top 32 eliminations
top_32_2
character
Top 32 eliminations (week 2)
top_32_3
character
Top 32 eliminations (week 3)
top_32_4
character
Top 32 eliminations (week 4)
top_30
character
Top 30 eliminations
top_30_2
character
Top 30 eliminations (week 2)
top_30_3
character
Top 30 eliminations (week 3)
top_25
character
Top 25 eliminations
top_25_2
character
Top 25 eliminations (week 2)
top_25_3
character
Top 25 eliminations (week 3)
top_24
character
Top 24 eliminations
top_24_2
character
Top 24 eliminations (week 2)
top_24_3
character
Top 24 eliminations (week 3)
top_20
character
Top 20 eliminations
top_20_2
character
Top 20 eliminations (week 2)
top_16
character
Top 16 eliminations
top_14
character
Top 14 eliminations
top_13
character
Top 13 eliminations
top_12
character
Top 12 eliminations
top_11
character
Top 11 eliminations
top_11_2
character
Top 11 eliminations (week 2)
wildcard
character
Wildcard week eliminations
comeback
logical
Comeback week eliminations
top_10
character
Top 10 eliminations
top_9
character
Top 9 eliminations
top_9_2
character
Top 9 eliminations (week 2)
top_8
character
Top 8 eliminations
top_8_2
character
Top 8 eliminations (week 2)
top_7
character
Top 7 eliminations
top_7_2
character
Top 7 eliminations (week 2)
top_6
character
Top 6 eliminations
top_6_2
character
Top 6 eliminations (week 2)
top_5
character
Top 5 eliminations
top_5_2
character
Top 5 eliminations (week 2)
top_4
character
Top 4 eliminations
top_4_2
character
Top 4 eliminations (week 2)
top_3
character
Top 3 eliminations
finale
character
Finale eliminations
finalists.csv
Contestant
character
Name of contestant
Birthday
character
Contestant’s birthday
Birthplace
character
Contestant’s city of birth
Hometown
character
Contestant’s hometown
Description
character
Description of contestant
Season
double
Season
ratings.csv
season
double
Season
show_number
double
Episode number in season
episode
character
Episode name
airdate
character
Date episode aired
18_49_rating_share
character
Percentage of adults aged 18-49 estimated to have watched the episode (Nielsen TV ratings).
viewers_in_millions
double
Number (in millions) that watched the episode
timeslot_et
character
Episode timeslot in Eastern Time
dvr_18_49
character
Percentage of adults aged 18-19 estimated to have watched the episode on DVR
dvr_viewers_millions
character
Number (in millions) that watched the episode on DVR
total_18_49
character
Total percentage of adults aged 18-49 estimated to have watched the episode
total_viewers_millions
character
Total number of viewers (in millions).
weekrank
character
Ranking of episode performance by season
ref
logical
Reference
share
character
share (unused)
nightlyrank
double
Nightly ranking
rating_share_households
character
Ranking per share of households.
rating_share
character
Ratings share.
seasons.csv
season
double
Season
winner
character
Name of winner
runner_up
character
Name of runner_up
original_release
character
Original air dates
original_network
character
Network aired on
hosted_by
character
Host’s name
judges
character
Name of judges
no_of_episodes
double
Episode name
finals_venue
character
Venue of finale
mentor
character
Name of season mentor
songs.csv
season
character
Season Number
week
character
Week date and week description
order
double
Order contestants sang in
contestant
character
Competitor name
song
character
Name of song sung
artist
character
Name of song’s artist (imputed if not explicitly listed)
song_theme
character
Week theme for songs sung
result
character
Contestant’s elimination status for the week
Cleaning Script
# Clean data provided by <https://github.com/kkakey/American_Idol>. No cleaning was necessary. <- readr:: read_csv ("https://raw.githubusercontent.com/kkakey/American_Idol/main/metadata/auditions.csv" )<- readr:: read_csv ("https://raw.githubusercontent.com/kkakey/American_Idol/main/metadata/elimination_chart.csv" )<- readr:: read_csv ("https://raw.githubusercontent.com/kkakey/American_Idol/main/metadata/finalists.csv" )<- readr:: read_csv ("https://raw.githubusercontent.com/kkakey/American_Idol/main/metadata/ratings.csv" )<- readr:: read_csv ("https://raw.githubusercontent.com/kkakey/American_Idol/main/metadata/seasons.csv" )<- readr:: read_csv ("https://raw.githubusercontent.com/kkakey/American_Idol/main/Songs/songs_all.csv" )